Amjad Masad & Adam D’Angelo: How Far Are We From AGI?

Summary

• Adam D’Angelo sees no LLM plateau: reasoning, code generation, and video improved so sharply in one year that computer-use objections and many current critiques should fade within another year or two. His practical AGI threshold is software better than a typical remote worker, and he expects “a very different world” five years out without requiring a fundamentally new architecture.
• Amjad Masad separates economically potent “functional AGI” from intelligence that can efficiently learn inside any new environment. Brute-force labeling, expert data, and contrived RL environments can automate many job components, but that dependence on “enormous effort and money and data” makes him doubt LLMs are naturally scaling toward true AGI or a singularity.
• Cheap digital labor could drive growth far beyond 4%-5%, but partial automation may first hollow out the human-capital pipeline. D’Angelo worries that experts could manage hundreds of agents while firms stop hiring entry-level workers; Masad adds that displacing experts could eventually undermine the data and expertise needed for further improvement.
• The scarce asset increasingly becomes whatever is missing from training data, creating demand for human knowledge, expert labeling, and verifiable RL environments. An AI might eventually prove every theorem from supplied axioms, D’Angelo argues, yet still cannot know “how did this particular company solve this problem 20 years ago?” unless someone records it.
• AI may simultaneously strengthen hyperscalers and unleash far more solo entrepreneurs, producing a barbell rather than a clean incumbent-versus-startup outcome. Competition among model suppliers keeps application costs falling, subscriptions monetize from day one, and weaker network effects permit multiple venture-scale winners—while founder-controlled incumbents are unusually capable of investing through disruption.
• Masad sees AI-assisted software creation as one of AI’s largest remaining unlocks, even though today’s vibe-coding tools remain far below professional engineering. His upside case is that anyone could eventually create what once required “a team of 100 professional software engineers”; he also wants more experimental research and less of Silicon Valley’s “get-rich driven” culture.
• Replit’s roadmap shows where agent economics are heading: longer autonomous loops, built-in verification, and parallel work across five to 10 agents before potentially reaching hundreds. Its progression ran from roughly two minutes for Agent V1 to 20 minutes for V2, an advertised 200 minutes for Agent 3, and actual user runs beyond 28 hours; Agent 4 is aimed at coordinated feature development and code merging.

Deep dive

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